A vehicle may have its speed controlled automatically to a desired speed via a controller with little input from a vehicle driver. One example way for a controller to regulate vehicle speed is to operate the vehicle in a cruise control mode. Cruise control mode may be described as a vehicle operating mode where vehicle speed is maintained within a desired vehicle speed range bounded via upper and lower vehicle speed thresholds without the driver requesting torque from a vehicle motive power source. The controller maintains vehicle speed within the desired speed range via adjusting torque output of the vehicle's motive power source. Thus, vehicle speed is maintained within a desired speed range via increasing and decreasing torque output of the vehicle motive power source. One way for the controller to maintain vehicle speed is to proportionately adjust torque output from the vehicle motive power source based an error in vehicle speed. The controller may apply a proportional/integral/derivative (PID) algorithm or some similar variant to adjust torque output of the vehicle motive power source and maintain vehicle speed within the desired vehicle speed range. However, PID vehicle speed control algorithms are reactionary in that they rely predominantly on a present or current vehicle speed error to provide a revised vehicle speed trajectory. As a result, and because vehicles are often operated in a higher gear in cruise control mode, the controller may make large changes in torque it requests from the vehicle's motive power source. The swings in requested torque may increase vehicle fuel consumption and disturb the driver.
The inventors herein have recognized the above-mentioned issue and have developed a vehicle system, comprising: a vehicle including a motive torque source; and a controller in the vehicle, the controller including executable instructions stored in non-transitory memory, the instructions including an adaptive nonlinear model predictive cruise control routine.
By adapting vehicle models and providing output from the adapted vehicle models to a nonlinear model predictive cruise control routine, it may be possible to provide the technical result of reducing vehicle torque demand swings while operating a vehicle in a cruise control mode. The torque swings may be reduced, at least in part, based on a priori road grade information. Further, adapting the vehicle model and a vehicle fuel consumption model real-time while the vehicle is in cruise control mode allows the nonlinear model predictive cruise control mode to adjust torque control strategy from a constant torque output to a pulse and glide torque output, thereby allowing multiple torque solution strategies from the controller for same driving conditions, excepting for changes in a vehicle fuel consumption model due to fuel properties or other changes in engine operating characteristics. The fuel economy optimal strategy is therefore selected automatically based on actual characteristic of the vehicle fuel consumption model.
The present description may provide several advantages. In particular, the approach may reduce the propensity for larger changes in requested vehicle torque to maintain vehicle speed. Additionally, the approach may reduce a vehicle's operating cost via reducing fuel consumption. Further, the approach may further reduce vehicle fuel consumption by actively requesting a transmission shift to neutral while operating the vehicle in cruise control mode.
The above advantages and other advantages, and features of the present description will be readily apparent from the following Detailed Description when taken alone or in connection with the accompanying drawings.
It should be understood that the summary above is provided to introduce in simplified form a selection of concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is defined uniquely by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure.